Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Liang, Huagen; Yang, Guijun; Yang, Hao; Xu, Bo; Li, Zhenhai; Yang, Xiaodong

doi:10.3389/fpls.2018.01638

Cited by 93 publications

(80 citation statements)

References 59 publications

(75 reference statements)

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“…There are some studies used vegetation indexes of UAV images as crop growth, crop vigor, or an indicator of crop leaf numbers [45][46][47]. Unfortunately, vegetation indexes such as the NDVI are easily saturated when crop plants are thick and the canopy is closed [48,49].…”

Section: Discussionmentioning

confidence: 99%

Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

Zhang

Bian

et al. 2019

Remote Sensing

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Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.

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Section: Discussionmentioning

confidence: 99%

Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

Zhang

Bian

et al. 2019

Remote Sensing

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“…The visible channel that is no longer captured by the modified RGB sensor is often captured by using another embedded RGB sensor. The use of both multispectral and visible sensors was observed in many cases [22,23,27,31,33,49,50,55,56,61,65,69,72,87,103,107,113,117].…”

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confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…As the number of articles on the application of UAVs to agricultural problems grows, it becomes more difficult to track the progress that is being made on the subject. To make matters even more complicated, the variety of targeted applications is high, including tasks such as stress detection and quantification [13][14][15][16][17], yield prediction [18][19][20][21][22][23][24], biomass estimation [20,[25][26][27], vegetation classification [28][29][30][31], canopy cover estimation [32][33][34][35][36], plant height assessment [37,38], and lodging [39,40], among others [41][42][43][44][45][46][47][48][49]. Each one of those applications has specific characteristics that must be taken into consideration to properly contextualize the impact of UAV-based technologies.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Barbedo

2019

Drones

230

117

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Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.

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Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach

Cited by 93 publications

References 59 publications

Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

A Review on UAV-Based Applications for Precision Agriculture

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

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